使用NCE作为损失函数，SGD优化，skipGram模式
# -*- coding: utf-8 -*-"""Created on Sat Jul 22 17:35:12 2017@author: bryan"""import collectionsimport mathimport osimport randomimport zipfileimport numpy as npimport urllibimport tensorflow as tfimport matplotlib.pyplot as pltfrom sklearn.manifold import TSNEurl='http://mattmahoney.net/dc/'def maybe_download(filename,expected_bytes): if not os.path.exists(filename): filename,_=urllib.request.urlretrieve(url+filename,filename) statinfo=os.stat(filename) if statinfo.st_size==expected_bytes: print('Found and verified', filename) else: print(statinfo.st_size) raise Exception('Failed to verify '+filename+' .Can you get to it with a browser?') return filenamefilename=maybe_download('text8.zip',31344016)def read_data(filename): with zipfile.ZipFile(filename) as f: data=tf.compat.as_str(f.read(f.namelist()[0])).split() return datawords=read_data(filename)print('Data size',len(words))vocabulary_size=50000def build_dataset(words): count=[['UNK',-1]] count.extend(collections.Counter(words).most_common(vocabulary_size-1)) dictionary=dict() for word,_ in count: dictionary[word]=len(dictionary) data=list() unk_count=0 for word in words: if word in dictionary: index=dictionary[word] else: index=0 unk_count+=1 data.append(index) count[0][1]=unk_count reverse_dictionary=dict(zip(dictionary.values(),dictionary.keys())) return data,count,dictionary,reverse_dictionarydata,count,dictionary,reverse_dictionary=build_dataset(words)del wordsprint('Most common words (+UNK)',count[:5])print('Sample data ',data[:10],[reverse_dictionary[i] for i in data[:10]])data_index=0def generate_batch(batch_size,num_skips,skip_window):#num_skips 为对每个单词生成多少个样本, skpi_window为单词最远可以联系的距离 global data_index assert batch_size%num_skips==0 assert num_skips<=2*skip_window batch=np.ndarray(shape=(batch_size),dtype=np.int32) labels=np.ndarray(shape=(batch_size,1),dtype=np.int32) span=2*skip_window+1 buffer = collections.deque(maxlen=span) for _ in range(span): buffer.append(data[data_index]) data_index=(data_index+1)%len(data) for i in range(batch_size//num_skips): target=skip_window targets_to_avoid=[skip_window] for j in range(num_skips): while target in targets_to_avoid: target=random.randint(0,span-1) targets_to_avoid.append(target) batch[i*num_skips+j]=buffer[skip_window] labels[i*num_skips+j,0]=buffer[target] buffer.append(data[data_index]) data_index=(data_index+1)%len(data) return batch,labels batch,labels=generate_batch(batch_size=8,num_skips=2,skip_window=1)for i in range(8): print(batch[i],reverse_dictionary[batch[i]],'->',labels[i,0],reverse_dictionary[labels[i,0]]) batch_size=128embedding_size=128 #生成的向量维度skip_window=1num_skips=2valid_size=16valid_window=100valid_examples=np.random.choice(valid_window,valid_size,replace=False)num_sampled=64 gragh=tf.Graph()with gragh.as_default(): train_inputs=tf.placeholder(tf.int32,shape=[batch_size]) train_labels=tf.placeholder(tf.int32,shape=[batch_size,1]) valid_dataset=tf.constant(valid_examples,dtype=tf.int32) with tf.device('/cpu:0'): embeddings=tf.Variable(tf.random_uniform([vocabulary_size,embedding_size],-1.0,1.0)) embed=tf.nn.embedding_lookup(embeddings,train_inputs) nce_weights=tf.Variable(tf.truncated_normal([vocabulary_size,embedding_size],stddev=1.0/math.sqrt(embedding_size))) nce_biases=tf.Variable(tf.zeros([vocabulary_size])) loss=tf.reduce_mean(tf.nn.nce_loss(weights=nce_weights, biases=nce_biases, labels=train_labels, inputs=embed, num_sampled=num_sampled, num_classes=vocabulary_size)) optimizer=tf.train.GradientDescentOptimizer(1.0).minimize(loss) norm=tf.sqrt(tf.reduce_sum(tf.square(embeddings),1,keep_dims=True)) normalized_embeddings=embeddings/norm valid_embeddings=tf.nn.embedding_lookup(normalized_embeddings,valid_dataset) similarity=tf.matmul(valid_embeddings,normalized_embeddings,transpose_b=True) init=tf.global_variables_initializer() num_steps=100001with tf.Session(graph=gragh) as session: init.run() print("Initialized") average_loss=0 for step in range(num_steps): batch_inputs,batch_labels = generate_batch(batch_size,num_skips,skip_window) feed_dict={train_inputs:batch_inputs,train_labels:batch_labels} _,loss_val=session.run([optimizer,loss],feed_dict=feed_dict) average_loss+=loss_val if step% 2000==0: if step>0: average_loss/=2000 print('Average loss at step',step,':',average_loss) average_loss=0 if step % 10000==0: sim=similarity.eval() for i in range(valid_size): valid_word=reverse_dictionary[valid_examples[i]] top_k=8 nearest=(-sim[i,:]).argsort()[1:top_k+1] log_str='Nearest to %s:' % valid_word for k in range(top_k): close_word=reverse_dictionary[nearest[k]] log_str='%s %s,' % (log_str,close_word) print(log_str) final_embeddings=normalized_embeddings.eval() def plot_with_labels(low_dim_embs,labels,filename='tsne.png'): assert low_dim_embs.shape[0] >= len(labels),'More labels than embeddings' plt.figure(figsize=(18,18)) for i , label in enumerate(labels): x,y=low_dim_embs[i,:] plt.scatter(x,y) plt.annotate(label, xy=(x,y), xytext=(5,2), textcoords='offset points', ha='right', va='bottom') plt.savefig(filename)tsne=TSNE(perplexity=30,n_components=2,init='pca',n_iter=5000)plot_only=100low_dim_embs=tsne.fit_transform(final_embeddings[:plot_only,:])labels=[reverse_dictionary[i] for i in range(plot_only)]plot_with_labels(low_dim_embs,labels,'F:\\learning\\tf\\tsne.png')
